Abstract
Python is a dynamic object-oriented programming language. Python provides strong support for integration with other programming languages and other tools. Python programming is rarely used in the field of artificial intelligence, especially artificial neural networks. This research focuses on running Python programming to recognize hiragana letters. In learning the character of Hiragana, one can experience difficulties because of the many combinations of vowels that form new letters by different means of reading and meaning. Discrete Hopfield network is a fully connected, that every unit is attached to every other unit. This network has asymmetrical weights. At Hopfield Network, each unit has no relationship with itself. Therefore it is expected that a computer system that can help recognize the Hiragana Images. With this pattern recognition Application of Hiragana Images, it is expected the system can be developed further to recognize the Hiragana Images quickly and precisely.
Highlights
Python comes with expandable standard libraries that can be learned
The Hopfield Network is an algorithm in artificial neural networks that can be used as recognition of Hiragana character patterns
To be able to read Hiragana character pattern using artificial neural network using Hopfield Network, the first step is analyzing by using image processing
Summary
Python comes with expandable standard libraries that can be learned. Many communities out there are developing Python libraries for various purposes. The problem encountered is how to make the machine able to recognize a picture of characters or handwriting strokes and translate it into the form of a particular pattern of letters or characters This character recognition function can be developed and implemented in the scanner software to support speed in performing character input or typing the desired character. This character recognition problem is solved by implementing artificial neural networks [5]. Lots of utilization obtained from the application of this Artificial Neural Network, such as pattern recognition on images, print letters, handwriting, sound, and others. Grayscale image intensity value (gray) is calculated from RGB image intensity value using equation
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